In most cases, one calculates average correlation matrices of a given time series data. A correlation matrix of time series data may vary depending on the length of observations and temporal position. Two well-known methods of estimating a true correlation matrix are; (i) the maximum likelihood estimation (MLE) and (ii) the graphical least absolute shrinkage and selection operator method (GLASSO). The MLE method first assumes a sample correlation matrix from a Gaussian distribution that is iteratively corrected to estimate an actual correlation matrix by maximizing the likelihood of observing the given time series data. The GLASSO method [1]} is an extension of the MLE method for those cases where MLE can not be applied, for example, if the dimensionality of the Gaussian distribution is higher than the available number of observation samples. Furthermore, a prerequisite of these techniques is to have information on time series data of all the network nodes, whereas, in real-world systems, often time series information of limited nodes are available. Therefore, these methods stall modelling cases where the number of time series is much lesser than the number of nodes forming the corresponding system.
